Sprint through
your recs with a

75% reduction

in workload

The matching problem

Financial Transactions Matching Problem

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Financial transaction data often suffer from quality issues and even with carefully configured rules can’t be reliably matched, leaving your reconciliation team to manually match thousands or even millions of transactions. Not only is this exhausting but your downstream processes have to wait for your teams to complete all this work before the reconciliations can be closed out.

 

Additionally, rules engine and user misjudgments sometimes lead to costly mismatches that may only be discovered much later in the overall operations workflow, requiring corrective actions in multiple downstream processes and systems.

The Matchimus solution

With its advanced match intelligence using specifically honed machine learning algorithms, Matchimus dramatically reduces your manual matching workload,  letting your team complete reconciliations faster and with less effort. In case-studies Matchimus typically delivers a 60-90% reduction in manual matching when compared to the market-leading rules engines. Even our competitors call our technology disruptive.

 

In addition to high match rates, Matchimus maintains a greater than 99.9% accuracy which virtually eliminates mismatches, keeping your recs and post-rec processes ship-shape.​

Matchimus Machine Learning Reconciliations

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